Good paper on credit assignment for agent RL. The main finding is that you want an LLM judge to choose where to check a trajectory, and the rollouts to decide how much credit that step gets. GRPO gives every token in a trajectory the same advantage, so the training signal cannot tell the decisive step from the rest. ProVer has a judge compare successful and failed rollouts and name the segment it thinks caused the difference. It then samples continuations from just before and just after that…